Childhood asthma is a devastating condition that incurs long-term medical and financial burdens for affected children and their families. But identifying young children at high risk to develop asthma has proven difficult. Current predictive models are simplistic and do not recognize the underlying complexity of asthma; however, more complex models tend to lose clinical utility. Yet such models are needed: asthma is one of the most common chronic childhood diseases, affecting seven million children in the United States alone. Recent studies suggest that clustering early childhood respiratory and allergy symptoms (i.e., wheezing, respiratory infections, atopic dermatitis) into distinct phenotypes may improve the ability to predict asthma development in children. However, little is known about the biologic risks underlying these phenotypes. Metabolic and mitochondrial dysfunction, for instance, has been associated with asthma, but critical gaps remain in our understanding of how it leads to development of the disease. Newborn metabolic screening is a public health initiative aimed at screening every child born for endocrine disorders and rare inborn errors of metabolism, including many disorders indicative of metabolic and mitochondrial dysfunction. This screening represents a unique data source that can be analyzed alongside perinatal and environmental exposures to further our understanding of asthma etiology. We hypothesize that individuals with mild metabolic disturbances at birth will be more prone to further metabolic and mitochondrial dysfunction leading to the development of respiratory and allergy phenotypes later in childhood when exposed to triggers in the environment or to other stressors. Using two prospective cohort studies from Tennessee designed to identify risk factors for asthma; we will address this hypothesis through the following specific study goal and aims. Specific Study Goal: Determine if inclusion of newborn metabolic screening data improves prediction of early childhood respiratory and allergy phenotypes. Aim 1: Identify clusters of respiratory phenotypes based on early childhood respiratory and allergy symptoms. Aim 2: Identify clinical, demographic, environmental and newborn metabolic screening metabolites that are predictive of infant respiratory morbidity and asthma and allergy phenotypic groups. Aim 3: Validate predictive models and determine the sensitivity and specificity of our model. Combining clinical and environmental data with data, such as neonatal screening measurements, routinely captured by state programs is a novel approach for creating predictive models that can be incorporated into clinically available tools, account for heterogeneity in asthma phenotypes, and uncover novel disease pathways. If this approach is successful, our predictive models will improve the diagnosis of asthma in childhood and possibly lead to prevention or strategies for early intervention of a significant illness that affects millions of children worldwide.